Quantifying mixing in flows transporting multiple scalars: New mixing metrics describing three-scalar mixing
Bibliographic record
Abstract
The present work investigates mixing metrics used to quantify multi-scalar mixing in turbulent flows. Whereas existing mixing metrics, such as the correlation coefficient or the segregation parameter, are capable of quantifying mixing between two scalars of interest, they fail to capture the interactions between these scalars and the surroundings in which they mix. To overcome such limitations, new multi-scalar mixing metrics derived from the invariants of a three-scalar unmixedness tensor are proposed and then evaluated in coaxial jets transporting multiple scalars. Application of these metrics in coaxial jets with varying momentum flux ratios provides clearer insights into the influence of initial flow conditions on scalar mixing, resolving contradictions observed using other metrics. In particular, these metrics can be used to concisely quantify the mixing of one scalar with another, as well as the mixing of the individual scalars with the surroundings (or, alternately, a third scalar field).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".